【Bilingual Data Scientist】In-house Product | Full Remote | JLPT N2～

★Bilingual Data Scientist / MLE | AI+Marketing

• Japanese: Business Level Required

◆ Location: Tokyo / Osaka

◆ Famous Venture Company

◆ Flextime & Hybrid Work

◆ In-house Products/Services

◆ Global Team

◆ Annual salary: 8 million yen - 14 million yen

-------------【About the company】-------------

The company provides a communication platform that leverages LINE to bridge the gap between businesses and their customers. They offer specialized solutions across a broad range of industries, including human resources, education, and real estate.

By placing their proprietary product at the core of their strategy, the company helps clients optimize communication to drive revenue growth and reduce operational costs. Currently, they are heavily focused on accelerating feature development and further enhancing their support ecosystem.

-------------【 Job Description】-------------

[Overview]

As a Senior Data Scientist, you will leverage advanced analytics, machine learning and statistical modeling to solve complex business problems in a data-driven way. The role requires deep expertise to turn data insights into practical strategies and strong communication skills to collaborate with cross-functional teams.

[Responsibilities]

- Ad message delivery optimization: develop and operate ML models to optimize personalized ad content and timing, predict Customer Lifetime Value (CLV), multi-channel customer modeling, customer segmentation and insight extraction from delivery results

- Personalization & recommendation: design and implement recommendation engines using rule-based, ML and deep learning approaches

- Advanced customer segmentation: lead customer behavior analysis using clustering techniques

- Data engineering & EDA: cleanse, preprocess and validate large, complex structured/unstructured datasets and define initial analytical strategies through EDA

- KPI design & stakeholder proposals: work with internal teams (sales, customer success) and client marketers to define business KPIs that are ambitious yet easy for non-specialists to understand

- Continuous improvement: identify model accuracy issues through validation and error analysis, and iterate on model structure and features

- Collaborative development & privacy: manage production-level repositories on GitHub with thorough version control and documentation, with attention to data privacy and AI ethics

- Research and apply cutting-edge technologies such as deep learning, NLP, LLMs and generative AI

[Team]

Data & AI team

[What We Expect]

- Lead the entire ML lifecycle, from data exploration to model implementation and maintenance, for core algorithms such as recommendation and ad delivery optimization

- Continuously improve model accuracy and value directly tied to business KPIs

- Translate business problems into data-driven approaches with management and business stakeholders

- Maximize team output by mentoring junior members and establishing development standards and documentation culture

[What You Will Gain]

- Value creation using a large, unique dataset combining the company's 1st-party data and clients' zero-party data

- Hands-on MLOps practice including feature pipelines, retraining and monitoring

- Experience leading data strategy and business KPI design with stakeholders

- Technical leadership experience in an English-first global team

[Career Path]

Tech lead / manager of DS/ML team, ML Architect / Principal Data Scientist, Data PdM / AI consultant, leading global AI/data projects

-------------【 Requirements】-------------

[Required]

- 4+ years of professional experience in data science

- Expertise in ML fundamentals for tabular and time-series data (prediction, classification, regression, clustering) and statistics; experience solving business problems with NumPy, Scikit-learn, PyTorch, etc.

- Python and SQL skills with attention to code quality, readability and maintainability

- Hypothesis-driven iteration, offline evaluation (e.g. cross-validation), A/B testing and basic statistical analysis

- End-to-end ML lifecycle experience, from data exploration and feature engineering to production operation

- Linux, VS Code, Git/GitHub, Docker and CI/CD (GitHub Actions, etc.); experience deploying ML models to production with engineering teams

- Japanese: Business level or above

- English: comfortable with technical conversations and willing to keep improving

[Preferred]

- SQL databases (MySQL, PostgreSQL), DWH/OLAP (Snowflake, Redshift)

- Cloud experience (AWS preferred, Azure, GCP)

- Recommender systems (collaborative/content-based filtering, matrix factorization, NCF, Two-Tower)

- Online serving, model deployment and MLOps; microservices (FastAPI, Flask); fine-tuning open-source DL models; high-QPS, low-latency ML APIs

- Airflow, AWS Step Functions, Kinesis, Kafka, Hadoop, Spark

- Ranking, search, ad delivery or personalization projects; understanding of growth metrics

- OSS contributions, publications or Kaggle achievements

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【Welfare】

・Transportation allowance (bicycle commuting permitted)

・Company-issued laptop provided

・Company-issued mobile phone provided ※For certain positions only

・Flexible start time system (may apply every 2 months)

・Social insurance (Health Insurance, Employees' Pension Insurance, Employment Insurance, Workers' Accident Compensation Insurance)

・Life and health support leave

・Health checkup costs covered

・Childbirth and childcare support system

・Club activities

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